ELSA ARCAUTE
University Collage London, UK
HUGUES BERSINI
FARI Institute of AI for the public goods, director of the IRIDIA AI Laboratory, Université Libre de Bruxelles, Belgium
What biology can teach us about the concept of "Identity
Abstract
tba
YAMIR MORENO
Director of the Institute for Biocomputation and Physics of Complex Systems (BIFI), Zaragoza, Spain
Social Dilemmas via Generative Agent-Based Models
Abstract
Current Large Language Models (LLMs) have opened new avenues for modeling complex social dynamics. In particular, LLM-driven agents provide a unique opportunity to explore several phenomena in artificial societies. Admittedly, recent advancements have demonstrated that LLMs can exhibit human-like behaviors, including cooperation, fairness, and adherence to social norms. However, they also present significant challenges, such as sensitivity to prompt design, hallucinations, and inconsistencies in decision-making. In this talk, we discuss the capacity of GABMs to reproduce behavioral experiments and compare findings on cooperation and reputation dynamics in human groups with those obtained by implementing a reputation-based game in which LLM-driven agents played the Prisoner’s Dilemma on dynamics networks. Our results indicate that LLM-based agents can partially reproduce human cooperative behavior and network dynamics, though important limitations remain. These models will become increasingly integrated into decision-making processes, thus, understanding their constraints and interaction patterns with humans and artificial agents is crucial.
CAMILLE ROTH
CNRS & EHESS, Centre d’analyse et de mathématique sociales, Paris, France
ELISA THÉBAULT
CNRS, Institut d’écologie et des science de l’environnement, Paris, France
Invited Speakers
GIULIA CENCETTI
Université Aix Marseille, Université de Toulon, France
GUILLAUME DEFFUANT
INRAE, Clermont Ferrand
TBA
Abstract
tba
Website
BENJAMIN FAGARD
CNRS Lattice Laboratory ENS
Identifying Language Change in a Corpus – A Frequency-Based Model
Abstract
The existence of large language models makes it possible to imagine new ways of looking at language change. But much can still be done by using existing language corpora and models of language change. For instance, looking at changes in frequency alone, it has been shown that it is possible to identify ongoing language change, e.g. the competition between variants, which displays a characteristic ‘s-curve’. This concept is anything but new (Osgood & Sebeok 1954, Kroch 1989), but has been recently refined theoretically with more explicit models (Blythe & Croft 2012, Feltgen et al. 2017, Feltgen 2024), and used to identify cases of language change in large corpora (Boukhaled et al. 2019). A crucial question in that respect is whether it can be used not only to identify language change, but to distinguish between different types of change (viz. lexical innovation, borrowing, calques, grammaticalization). In my talk, I will address this question with data from large diachronic corpora.
References
Blythe, R. A. & W. Croft. 2012. S-curves and the mechanisms of propagation in language change. Language 88(2), 269-304.
Boukhaled, M.A., Fagard, B. & T. Poibeau. 2019. The Dynamics of Semantic Change: A Corpus-Based Analysis. Lecture Notes in Artificial Intelligence 11978, 1-15.
Feltgen, Q., B. Fagard & J.-P. Nadal. 2017. Frequency patterns of semantic change: Corpus-based evidence of a near-critical dynamics in language change. Royal society open science.
Feltgen, Quentin. 2024. Is language change chiefly a social diffusion affair? The role of entrenchment in frequency increase and in the emergence of complex structural patterns. Front. Complex Syst. 2:1327425.
Kroch, Anthony S. 1989. Reflexes of grammar in patterns of language change. Language variation and change 1(3), 199–244.
AGNIESZKA RUSINOWSKA
CNRS, CES, PSE, Université Paris 1, Paris, France